Free Databricks Databricks-Certified-Machine-Learning-Associate Exam Questions

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.

Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?